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Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering
Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected cl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371436/ https://www.ncbi.nlm.nih.gov/pubmed/35957463 http://dx.doi.org/10.3390/s22155906 |
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author | Zhu, Xing-Hui Zhou, Yi Yang, Meng-Long Deng, Yang-Jun |
author_facet | Zhu, Xing-Hui Zhou, Yi Yang, Meng-Long Deng, Yang-Jun |
author_sort | Zhu, Xing-Hui |
collection | PubMed |
description | Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected clustering with a spatial–spectral constrained adaptive graph (PCSSCAG) method for HSI clustering. PCSSCAG first constructs an adaptive adjacency graph to capture the accurate local geometric structure of HSI data adaptively. Then, a spatial–spectral constraint is employed to simultaneously explore the spatial and spectral information for reducing the negative influence on graph construction caused by noise. Finally, projection learning is integrated into the spatial–spectral constrained adaptive graph construction for reducing the redundancy and alleviating the computational cost. In addition, an alternating iteration algorithm is designed to solve the proposed model, and its computational complexity is theoretically analyzed. Experiments on two different scales of HSI datasets are conducted to evaluate the performance of PCSSCAG. The associated experimental results demonstrate the superiority of the proposed method for HSI clustering. |
format | Online Article Text |
id | pubmed-9371436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93714362022-08-12 Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering Zhu, Xing-Hui Zhou, Yi Yang, Meng-Long Deng, Yang-Jun Sensors (Basel) Article Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected clustering with a spatial–spectral constrained adaptive graph (PCSSCAG) method for HSI clustering. PCSSCAG first constructs an adaptive adjacency graph to capture the accurate local geometric structure of HSI data adaptively. Then, a spatial–spectral constraint is employed to simultaneously explore the spatial and spectral information for reducing the negative influence on graph construction caused by noise. Finally, projection learning is integrated into the spatial–spectral constrained adaptive graph construction for reducing the redundancy and alleviating the computational cost. In addition, an alternating iteration algorithm is designed to solve the proposed model, and its computational complexity is theoretically analyzed. Experiments on two different scales of HSI datasets are conducted to evaluate the performance of PCSSCAG. The associated experimental results demonstrate the superiority of the proposed method for HSI clustering. MDPI 2022-08-07 /pmc/articles/PMC9371436/ /pubmed/35957463 http://dx.doi.org/10.3390/s22155906 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Xing-Hui Zhou, Yi Yang, Meng-Long Deng, Yang-Jun Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title | Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title_full | Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title_fullStr | Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title_full_unstemmed | Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title_short | Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering |
title_sort | spatial–spectral constrained adaptive graph for hyperspectral image clustering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371436/ https://www.ncbi.nlm.nih.gov/pubmed/35957463 http://dx.doi.org/10.3390/s22155906 |
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